




'''simple docstring'''


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class   __A (							a  ):



   """simple docstring"""







   def __init__(      self				, _lowerCamelCase				, _lowerCamelCase						)->       List[Any]:
      lowercase__       							=							params

      lowercase__       							=							np.array(_lowerCamelCase						)
      lowercase__       							=							np.array([len(_lowerCamelCase						) for t in data]						)

      self.check()
      self.remove_long_sequences()
      self.remove_empty_sequences()
      self.remove_unknown_sequences()
      self.check()
      self.print_statistics()

   def __getitem__(      self				, _lowerCamelCase						)->       List[str]:
      return (self.token_ids[index], self.lengths[index])

   def __len__(      self						)->       Optional[Any]:
      return len(self.lengths						)

   def snake_case_(      self						)->       str:
      assert len(self.token_ids						) == len(self.lengths						)
      assert all(self.lengths[i] == len(self.token_ids[i]						) for i in range(len(self.lengths						)						)						)

   def snake_case_(      self						)->       Union[str, Any]:
      lowercase__       							=							self.params.max_model_input_size
      lowercase__       							=							self.lengths > max_len
      logger.info(f'''Splitting {sum(_lowerCamelCase						)} too long sequences.'''						)

      def divide_chunks(_lowerCamelCase				, _lowerCamelCase						):
         return [l[i : i + n] for i in range(0				, len(_lowerCamelCase						)				, _lowerCamelCase						)]

      lowercase__       							=							[]
      lowercase__       							=							[]
      if self.params.mlm:
         lowercase__							,					lowercase__       							=							self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
      else:
         lowercase__							,					lowercase__       							=							self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

      for seq_, len_ in zip(self.token_ids				, self.lengths						):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
            new_tok_ids.append(seq_						)
            new_lengths.append(len_						)
         else:
            lowercase__       							=							[]
            for sub_s in divide_chunks(seq_				, max_len - 2						):
               if sub_s[0] != cls_id:
                  lowercase__       							=							np.insert(_lowerCamelCase				, 0				, _lowerCamelCase						)
               if sub_s[-1] != sep_id:
                  lowercase__       							=							np.insert(_lowerCamelCase				, len(_lowerCamelCase						)				, _lowerCamelCase						)
               assert len(_lowerCamelCase						) <= max_len
               assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
               sub_seqs.append(_lowerCamelCase						)

            new_tok_ids.extend(_lowerCamelCase						)
            new_lengths.extend([len(_lowerCamelCase						) for l in sub_seqs]						)

      lowercase__       							=							np.array(_lowerCamelCase						)
      lowercase__       							=							np.array(_lowerCamelCase						)

   def snake_case_(      self						)->       Tuple:
      lowercase__       							=							len(self						)
      lowercase__       							=							self.lengths > 1_1
      lowercase__       							=							self.token_ids[indices]
      lowercase__       							=							self.lengths[indices]
      lowercase__       							=							len(self						)
      logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''						)

   def snake_case_(      self						)->       str:
      if "unk_token" not in self.params.special_tok_ids:
         return
      else:
         lowercase__       							=							self.params.special_tok_ids['''unk_token''']
      lowercase__       							=							len(self						)
      lowercase__       							=							np.array([np.count_nonzero(a == unk_token_id						) for a in self.token_ids]						)
      lowercase__       							=							(unk_occs / self.lengths) < 0.5
      lowercase__       							=							self.token_ids[indices]
      lowercase__       							=							self.lengths[indices]
      lowercase__       							=							len(self						)
      logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''						)

   def snake_case_(      self						)->       Dict:
      if not self.params.is_master:
         return
      logger.info(f'''{len(self						)} sequences'''						)
      # data_len = sum(self.lengths)
      # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
      # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

      # unk_idx = self.params.special_tok_ids['unk_token']
      # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
      # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')

   def snake_case_(      self				, _lowerCamelCase						)->       Dict:
      lowercase__       							=							[t[0] for t in batch]
      lowercase__       							=							[t[1] for t in batch]
      assert len(_lowerCamelCase						) == len(_lowerCamelCase						)

      # Max for paddings
      lowercase__       							=							max(_lowerCamelCase						)

      # Pad token ids
      if self.params.mlm:
         lowercase__       							=							self.params.special_tok_ids['''pad_token''']
      else:
         lowercase__       							=							self.params.special_tok_ids['''unk_token''']
      lowercase__       							=							[list(t.astype(_lowerCamelCase						)						) + [pad_idx] * (max_seq_len_ - len(_lowerCamelCase						)) for t in token_ids]
      assert len(tk_						) == len(_lowerCamelCase						)
      assert all(len(_lowerCamelCase						) == max_seq_len_ for t in tk_						)

      lowercase__       							=							torch.tensor(tk_						)  # (bs, max_seq_len_)
      lowercase__       							=							torch.tensor(_lowerCamelCase						)  # (bs)
      return tk_t, lg_t










'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



